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Jasindo Insurance Company is one of the insurance companies that receives insurance coverage both directly and indirectly, with ownership of 1 share of dwiwarna series A owned by the Republic of Indonesia and 424,999 shares of Series B owned by PT Bahana Pembinaan Usaha Indonesia (Persero). PT Asuransi Jasa Indonesia has several products and choices in choosing which insurance is needed by customers in agriculture, health, education and many more. Due to the large amount of competition in the business world, it requires management to find the right strategy in increasing the use of Jasindo insurance by knowing the relationship between age, gender, marital status, occupation and the type of Jasindo insurance that is widely chosen by customers. In order to find out the use of insurance that is widely used by the community, it is necessary to analyze the data on the use of insurance using the apriori algorithm method to determine the combination between item-sets of transaction data on Jasindo insurance data. Based on the research conducted after experimenting with the above case with a minimum support = 25%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained, namely if the gender is male, the marital status is Unmarried, then the type of insurance is jasimdo health and jasindo rainbow then giving value is successful with 25% support, 100% confidence.  

Police Sector (Polsek) is one of the agencies that provide protection, order and ensure public safety in the sunggal area. The number of cases of criminal acts that occur makes residents feel unsafe and always feel threatened in certain areas in the Sunggal sub-district, the pattern of criminal acts that often occur due to several factors, one of which is due to the lack of security in the area so that many criminal acts occur as well as behaviour that has been planned by the perpetrator to achieve their goals by planning, preparing, implementing, disposing of evidence, even hiding or escaping depending on the type of crime committed based on the characteristics of the perpetrator, and the situation or context in which the crime occurred. Therefore, it is necessary to analyse techniques from existing criminal data using the a priori algorithm method to find patterns of relationships between variables that can assist agencies in taking action for public safety. Based on the research conducted, the above case is tested with a minimum support = 10%, confidence = 100% so that the results of the rule that meets the support and confidence values are obtained: ‘If the criminal act is theft then the job is self-employed’, then giving value is successful with 15% support, 100% confidence. And ‘If the age of 17-25 years, the criminal act is Theft then the job is unemployed’, then giving value is successful with 10% support, 100% confidence.

Student behavior is the actions of students which are influenced by their attitudes and responsibilities at school. Student behavior is a very important factor in determining student achievement in learning. Students who have personalities that improve skills, knowledge, attitudes, habits, understanding, skills, thinking power and other abilities will more easily increase their concentration in learning, and it will be easier to achieve the students' goals. At SDN 053960 MARYKE there are still students who do not know that student behavior greatly influences their level of achievement. Therefore, it is necessary to educate students from an early age so that students can be more responsible for the rules given by teachers at school, and students must understand that the attitude they carry out at school is assessed in improving their achievement, as well as their presence is very influential. his level of achievement at school. Therefore, there is a need for a solution to overcome the problems that exist at SDN 053960 MARYKE by utilizing data mining to collect data and then it will be processed using the a priori method with variables contained in the correlation between student behavior and student achievement levels. The a priori algorithm is able to determine min support and confidence in these variables will later show the relationship between student behavior and student achievement levels, so that researchers will get the best best rules and be able to produce the latest information..

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

PT Astra International BMW Semarang operates in the automotive sector, focusing on sales, aftersales, and spare parts for BMW cars. The availability of spare parts is crucial for customer satisfaction, as stock shortages can lead to disappointment. Using data from 52,162 spare parts sales transactions from January 2019 to June 2023, the study applies data mining techniques with the a priori and eclat algorithms to identify consumer purchasing patterns and prevent stock shortages. The research aims to provide recommendations for prioritizing spare parts stock. Utilizing the CRISP-DM methodology and R programming, the study found that the highest confidence in purchasing patterns occurs with a combination of three itemsets: if a customer buys an oil filter set (B11.42.8.593.186) and washer cleaner (B83.12.5.A1A.683), they will also buy BMW engine oil (Z99000000333) with 100% confidence. These findings can help PT Astra International BMW Semarang manage spare parts stock more effectively.

All PT Astra International BMW Semarang transactions are recorded in the database but the problem is that the stock management is  efficientless so  the part stock that buyers are interested is not available. This research aims to conduct a comparative mining results using the association rule with apriori algorithm for year 2021, 2022 and 2023 sales transaction dataset with total of 43.694 records using the Rstudio. Data mining process in each year uses the same parameters for each itemset combination. The best association pattern occurs in 2023 with support value 0.05913841 and confidence value 100%. This can be concluded that the rules formed from each year could be different eventhough using same parameters. The item that always appears in the association rule from 2021 – 2023is Z99000000333 (BMW Engine OIL) which is often purchased with items named “Set fil-oil” so it can be a recommendation for  item stocking  in the warehouse.

Data mining is a technique for extracting new information from data warehouses, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as is the case with the Dimsum business at Dimsum Madani.toko. This is located on Jalan Lampu gg. Pelita 4, Brayan Bengkel, East Medan, the location is close to many Brayan Resident's Houses. This of course affects sales levels. Increasing daily sales activity results in an accumulation of sales transaction data that continues to increase, thereby burdening data storage. Unfortunately, this data is only stored without further processing. In fact, this data collection holds valuable information.This research uses Market Basket Analysis with the Apriori Algorithm to find association patterns based on consumer shopping behavior. The goal is to identify items that are often purchased together. The research results showed that the combination of Seaweed Dimsum with Tofu Skin Spring Rolls had the highest support value (50%) and the highest confidence (75%).

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

Data mining is a technique to dig up new information from a data warehouse, information is seen as very important and valuable because by mastering information it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in trading businesses such as the Iblite Luxury store.  This store is located in Medan close to residents' houses, Sales transaction data will continue to grow, causing data storage to be even larger. Sales transaction data is only used as an archive without being properly utilized. Basically, a dataset has very useful information. Market basket analysis with a priori algorithm is one of the data mining methods that aims to find association patterns based on consumer shopping patterns, so that it can be known what items of goods are purchased in a At the same time, the results of this study found that the highest support and confidence values were Ysl and Chanel with a support value of 50% and confidence of 75%.

Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.

This study aims to analyze purchasing patterns in online transactions using the Apriori algorithm to support sales strategy optimization. The research was conducted on transactional data from an online store selling household and daily-use products. The Apriori method was applied to identify associations between items based on minimum support and confidence thresholds. Four experimental scenarios were tested to compare the reliability of generated rules and determine the strongest item relationships. Data preprocessing included item grouping, transaction coding, and elimination of non-frequent items. The results show several strong association rules with lift ratio values above 1, indicating meaningful item relationships. The strongest rule identified was the association between forks and spoons, forming a highly relevant combination for product bundling strategies. The findings demonstrate that the Apriori algorithm can assist online stores in planning stock, designing product bundling, and improving marketing effectiveness. The research contributes practical insights for business decision-making and highlights the significance of data mining in e-commerce environments.

The aim of this research is to apply an a priori algorithm to determine vegetable purchasing patterns and analyze the results in order to control vegetable stocks at Sawargaloka Hydroponic Hydrofarm. The need for quality and safe food supplies is increasing along with population growth, where plants are grown without using land, but using nutrient solutions that are rich in important substances, the application of data mining using the Apriori method can provide valuable insight into the purchasing patterns of hydroponic vegetables by customers. By understanding these patterns, companies can improve marketing strategies, plan production more efficiently, and provide product recommendations to customers. The results of analytical research using the Apriori method on hydroponic vegetable purchase data at Sawargaloka Hydrofarm, it can be concluded that the application of data mining has great potential in identifying significant purchasing patternsa

Data mining is a technique to extract new information from the data warehouse, information is considered very important and valuable because by mastering the information so easily to achieve a goal, this makes everyone competing to obtain information, as well as on trading businesses such as bag store BRANDED. store is located close to the home of the population, this certainly affects the level of sales, with the daily sales activities, sales transaction data will continue to grow, causing data storage is greater. Sales transaction data is only used as an archive without being put to good use. Basically the data set has very useful information. The analysis of market basket with Apriori Algorithm is one method of data mining which aims to find the pattern of association based on consumer spending pattern, so that it can be known what items are purchased simultaneously. The result of this research found that the highest support and confidence value is Ysl and Chanel with a support value of 50% and confidence of 75%.

This study aims to determine the best selling food and beverage products at Caffe Kopi Kito. Data mining is the process of extracting useful information and patterns from very large data. Data mining includes data collection, data extraction, data analysis, and data statistics. The Apriori algorithm is a classic algorithm in data mining. This algorithm is used to see the intensity of occurrence of the relevant itemset or frequent items or association rules. This study uses consumer transaction data for 30 days in January 2023. Transaction data will be collected first based on the day and number of transactions, then the transaction data that has been collected will be grouped according to each item, the data that has been grouped will be carried out a priori algorithm process to determine the most dominant product. Then a system design will be carried out whose result will be a website. The results showed that using the website-based a priori algorithm could determine the most dominant product at Caffe Kopi Kito and make it easier for users to determine the most dominant product. Based on the results of product sales analysis at Cafee Kopi Kito, it can be concluded that working on the a priori algorithm on Caffe Kopi Kito using a website can be said to have the result of a product combination and in the future it can be used to create the best-selling menu packages at Cafee Kopi Kito.

This data mining association processes 1224 Gamefantasia ticket redemption transaction data. The goal is to find a pattern of association between goods as a recommendation for structuring the display of goods at the cashier counter and increasing ticket exchange transactions. Modeling uses a comparison of two algorithms, namely the Apriori algorithm and FP-Growth. The data analysis method with the CRISMP-DM method is then processed by RStudio software. The results of the study with the same parameters support 0.02 and confidence 0.1 FP-Growth algorithm formed 53 rules, the strength of the association rule 6.2%, the accuracy was1245%. Whereas the Apriori algorithm forms only 12 rules, the strength of the association rules is 2.1% and the accuracy is 7.8%. Thus, it can be concluded that the use of the FP-Growth algorithm has better results than the Apriori algorithm because it has the highest accuracy in finding transaction patterns.

This The speed of information, the accuracy of data, the ease of information services, and accountability are very important reasons for the implementation of the system. Semarang University (USM) is a private university in Semarang that has the most 2 students in Central Java. Based on the 2019 USM tracer data showing horizontal alignment, namely how close the relationship between the field of study and alumni work is, it appears that there is still a discrepancy in the ability of graduates with stakeholders.  The Apriori algorithm is the best-known algorithm for finding high-frequency patterns  Rules that state associations between attributes are often called affinity analysis or market basket analysis. The use of the Apriori Algorithm in data mining calculations using data from the Semarang University tracer that the limit of the minimum support is 50% and the minimum confidence is 100% so that it forms 4 rules. From the four rules produced that modeling using the Apriori Algorithm can produce several rule formations so that it can provide an evaluation to the University for compiling steps, this can be seen because the resulting rules are different because each graduate relationship with the desired desires and different styles.  

Discount is a sales strategy that lowers the price offered to buyers in the hope of increasing sales profits. However, when offering these discounts, stores often don't think about how discounted products can attract customers, so a support system is needed that can respond to the right and appropriate discounts. This support system with data mining techniques using apriori algorithm. This apriori method can search for several products that are often purchased simultaneously, therefore when juxtaposed with this study the effectiveness of the recommendations will be very good. Not only that, if it is added with a combination of recommendations with discounts, it will make customers interested to buying the product. The expected result of this research is to obtain a new sales strategy by making recommendations on items that will be used as discount packages.

ABSTRACT The rapid development of technology has a very large effect in various fields. One of them is in the field of buying and selling business, which is getting higher and higher competition between business actors. One of the strategies to increase sales is to implement a recommendation for goods, but from the various categories of goods in the store, there are products that are not in demand by customers, so that if left alone, the products that are not in demand will not sell well and will make the accumulation of goods in the store, in addition to that with the many categories, it makes some customers confused to choose products that suit their wishes buyer. Therefore, the author conducts an assessment first by conducting a literature study, which finally the purpose of this study is to make it easier for users and business owners to recommend goods in the store and determine the desired product by implementing a recommendation system on the Rameiki Mart Store website which is taken from the amount of data, this is also beneficial for store owners because the existence of this recommendation system can help as a means of product promotion , as well as in recommendations for the purchase of goods. In this study, the researcher created a recommendation system using the a priori algorithm method.

Dunia bisnis khususnya dalam industri penjualan dimana-mana tidak di ambil kemungkinan banyak resiko yang di hadapi pembisnis untuk bisa melangsungkan usaha yang telah di dirikan akan selalu ada dan mendapatkan konsumen yang tetap membeli barang yang telah disediakan maka dari itu seorang entrepreneur dituntut untuk memiliki strategi dalam membaca peluang. Untuk menyiasati hal tersebut, tentunya pihak manajemen harus mampu menganalisa data yang ada untuk dijadikan bahan acuan untuk strategi diperlukan untuk komputerisasi. Pencarian judul penelitian dan abstraknya dipermudah dengan kata-kata kunci tersebut. berbisnis selanjutnya. Meubel Master borneo merupakan salah satu perusahaan yang memiliki resiko mendapatkan konsumen yang tetap dan harus memberikan atau meyediakan barang yang memiiki kualitas tinggi dan memberikan pelayanan yang akan diberikan kepada pelanggan yang setia membeli produk yang telah disediakan. Dengan menggunakan data mining yang merupakan knowledge discovery dikarenakan bidang yang berupaya untuk menemukan informasi yang memiliki arti yang berguna dari jumlah data yang besar, untuk menemukan pola (pattern) data dan memprediksi kelakuan (trend) dimasa mendatang [7]. Untuk mengetahui produk yang sering terjual dalam periode bulan Januari sampai bulan Mei 2019 diperlukan algoritma apriori yang ada di data mining. Dengan melakukan analisa keranjang belanja menggunakan metode asosiasi dengan Algoritma Apriori, dimana kombinasi itemset transaksi penjualan barang pada meubel master borneo menghasilkan 6 rules dimana minimum confidence sebesar 41,6 % dan minimum support sebesar 0,08% berdasarkan 35 transaksi penjualan dari 63 jenis barang pada meubel Master Borneo.

Along with the development of the era, also followed by growth and also the birth of many companies in the field of goods and services, where each company always strives as much as possible to obtain and maintain market share. This can make competition tight, especially for business people, especially those that occur in digital printing companies owned by Abadi Digital Printing, Salatiga. Because this printing company is still a new company, it requires a lot of research on consumer purchasing patterns to increase sales and marketing strategies so as not to be rivaled by other printing that has been longer, the following analysis uses apriori algorithms with RapidMiner tools. By using a support value of 0,025 and confidence of 0,6, the results are that the items often purchased by consumers are standing banner with  x banner. From these results it can be used as a promotional event, deal packages, etc to increase consumer attractiveness.